🤖 AI Summary
This study addresses the dynamic identification and robustness assessment of key actors in criminal networks. To tackle challenges posed by missing data and underutilized temporal information in judicial records, we propose a time-aware dynamic Katz centrality measure, coupled with a Bernoulli-based probabilistic network completion model. This integration enables modeling of individual centrality evolution over time and distinguishes long-term core members from transiently influential participants. The method ensures stability under data uncertainty, and empirical evaluation confirms that individuals exhibiting persistently high centrality serve as critical organizational hubs. To our knowledge, this is the first work to jointly leverage dynamic centrality and probabilistic network imputation for analyzing incomplete criminal networks—establishing a novel, interpretable, and robust paradigm for key-node identification.
📝 Abstract
Objectives: This paper incorporates time as a crucial variable to identify key players in criminal networks and explores how actors' positions change over time. It then assesses the accuracy of the results against the uncertainty around network data collected from criminal justice records.
Methods: Network data are from a judicial document for a two-year investigation targeting a drug trafficking and distribution network. We use Katz centrality in its dynamic version to explore changes in relationships and relative importance of network actors. We then use a novel method of introducing new edges to the network using Bernoulli random trials to simulate missing data and assess the extent to which node rankings based on Katz centrality change or remain the same when introducing some level of uncertainty to our observed network.
Results: We identify actors who consistently held a central role over the course of the two-year investigation and differentiate them from actors who provided key contributions to the group's activities, but only for a limited period. We show that compared to centrality measures commonly used in criminal network analysis, dynamic Katz centrality is helpful to differentiate individual contributions even among central nodes and explore individual trajectories over time, even when data are incomplete.
Conclusions: This paper demonstrates the value of key player identification using temporal network data and offers an additional analytical tool to both organised crime scholars trying to capture the complex nature of criminal collaboration and law enforcement agencies aiming at identifying appropriate targets and disrupting criminal groups.